{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistical inference of stock returns with linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports & Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:16:01.990515Z",
     "start_time": "2020-06-17T19:16:01.987955Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:16:02.574792Z",
     "start_time": "2020-06-17T19:16:01.991964Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from statsmodels.api import OLS, add_constant, graphics\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "from scipy.stats import norm\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:16:02.577751Z",
     "start_time": "2020-06-17T19:16:02.575748Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')\n",
    "idx = pd.IndexSlice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:16:04.405437Z",
     "start_time": "2020-06-17T19:16:02.578559Z"
    }
   },
   "outputs": [],
   "source": [
    "with pd.HDFStore('data.h5') as store:\n",
    "    data = (store['model_data']\n",
    "            .dropna()\n",
    "            .drop(['open', 'close', 'low', 'high'], axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select Investment Universe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:16:04.425978Z",
     "start_time": "2020-06-17T19:16:04.406353Z"
    }
   },
   "outputs": [],
   "source": [
    "data = data[data.dollar_vol_rank<100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:16:04.449734Z",
     "start_time": "2020-06-17T19:16:04.427286Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 109675 entries, ('AAL', Timestamp('2013-07-25 00:00:00')) to ('ZTS', Timestamp('2014-12-04 00:00:00'))\n",
      "Data columns (total 64 columns):\n",
      " #   Column                 Non-Null Count   Dtype  \n",
      "---  ------                 --------------   -----  \n",
      " 0   volume                 109675 non-null  float64\n",
      " 1   dollar_vol             109675 non-null  float64\n",
      " 2   dollar_vol_rank        109675 non-null  float64\n",
      " 3   rsi                    109675 non-null  float64\n",
      " 4   bb_high                109675 non-null  float64\n",
      " 5   bb_low                 109675 non-null  float64\n",
      " 6   atr                    109675 non-null  float64\n",
      " 7   macd                   109675 non-null  float64\n",
      " 8   return_1d              109675 non-null  float64\n",
      " 9   return_5d              109675 non-null  float64\n",
      " 10  return_10d             109675 non-null  float64\n",
      " 11  return_21d             109675 non-null  float64\n",
      " 12  return_42d             109675 non-null  float64\n",
      " 13  return_63d             109675 non-null  float64\n",
      " 14  return_1d_lag1         109675 non-null  float64\n",
      " 15  return_5d_lag1         109675 non-null  float64\n",
      " 16  return_10d_lag1        109675 non-null  float64\n",
      " 17  return_21d_lag1        109675 non-null  float64\n",
      " 18  return_1d_lag2         109675 non-null  float64\n",
      " 19  return_5d_lag2         109675 non-null  float64\n",
      " 20  return_10d_lag2        109675 non-null  float64\n",
      " 21  return_21d_lag2        109675 non-null  float64\n",
      " 22  return_1d_lag3         109675 non-null  float64\n",
      " 23  return_5d_lag3         109675 non-null  float64\n",
      " 24  return_10d_lag3        109675 non-null  float64\n",
      " 25  return_21d_lag3        109675 non-null  float64\n",
      " 26  return_1d_lag4         109675 non-null  float64\n",
      " 27  return_5d_lag4         109675 non-null  float64\n",
      " 28  return_10d_lag4        109675 non-null  float64\n",
      " 29  return_21d_lag4        109675 non-null  float64\n",
      " 30  return_1d_lag5         109675 non-null  float64\n",
      " 31  return_5d_lag5         109675 non-null  float64\n",
      " 32  return_10d_lag5        109675 non-null  float64\n",
      " 33  return_21d_lag5        109675 non-null  float64\n",
      " 34  target_1d              109675 non-null  float64\n",
      " 35  target_5d              109675 non-null  float64\n",
      " 36  target_10d             109675 non-null  float64\n",
      " 37  target_21d             109675 non-null  float64\n",
      " 38  year_2014              109675 non-null  uint8  \n",
      " 39  year_2015              109675 non-null  uint8  \n",
      " 40  year_2016              109675 non-null  uint8  \n",
      " 41  year_2017              109675 non-null  uint8  \n",
      " 42  month_2                109675 non-null  uint8  \n",
      " 43  month_3                109675 non-null  uint8  \n",
      " 44  month_4                109675 non-null  uint8  \n",
      " 45  month_5                109675 non-null  uint8  \n",
      " 46  month_6                109675 non-null  uint8  \n",
      " 47  month_7                109675 non-null  uint8  \n",
      " 48  month_8                109675 non-null  uint8  \n",
      " 49  month_9                109675 non-null  uint8  \n",
      " 50  month_10               109675 non-null  uint8  \n",
      " 51  month_11               109675 non-null  uint8  \n",
      " 52  month_12               109675 non-null  uint8  \n",
      " 53  capital_goods          109675 non-null  uint8  \n",
      " 54  consumer_durables      109675 non-null  uint8  \n",
      " 55  consumer_non-durables  109675 non-null  uint8  \n",
      " 56  consumer_services      109675 non-null  uint8  \n",
      " 57  energy                 109675 non-null  uint8  \n",
      " 58  finance                109675 non-null  uint8  \n",
      " 59  health_care            109675 non-null  uint8  \n",
      " 60  miscellaneous          109675 non-null  uint8  \n",
      " 61  public_utilities       109675 non-null  uint8  \n",
      " 62  technology             109675 non-null  uint8  \n",
      " 63  transportation         109675 non-null  uint8  \n",
      "dtypes: float64(38), uint8(26)\n",
      "memory usage: 35.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info(null_counts=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Model Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:16:04.470622Z",
     "start_time": "2020-06-17T19:16:04.451344Z"
    }
   },
   "outputs": [],
   "source": [
    "y = data.filter(like='target')\n",
    "X = data.drop(y.columns, axis=1)\n",
    "X = X.drop(['dollar_vol', 'dollar_vol_rank', 'volume', 'consumer_durables'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:16:04.796592Z",
     "start_time": "2020-06-17T19:16:04.472130Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ldcXe+3/6zxuvX/A9ZF3SKnk8Hk197Q3dff8f9N9/zJYkpe3coYF3/E5/+/sr+tvfX1HXm3vyHnIZsYOMq1561ik9E/+x/nJ7b0lSs4jqStlzUJK0dudedYisJ7/lV+r+Qyrw+VXg8+hg1gk1rnmttqdnFD3PxczleQoUEhQkm03KL/Dqzpi2WvT1ptI/eVyUG7r30A039Sg6djgcys/L0+/uGaHkb9aecyaibl35/D75/X7l5eTK4Sx8292+dYsaNG6sSeMfU/VatTTioYclFe44F3g8CnK5dOTQIbnz81S/UeNzPjfKn5tbN1HKnoO6oWkDSdL/1m2RpcJwrhDiUq7bU+zxx7NzZbMXJrDL6ZTfKtxfDglyqnurSC1P3al+17eQJPn8fjnsNjkddnn9flUOC1GQw66jp3NK7wTxi3Tq2k3tb4iRJGVmHFHlKlUu+B7yXepmRXfsJElq1qq1du3YLknatWO70g/s17rVq1SrTl2NeOhhBblcvIdcJpccyHPnzpXb7b6cawHKROJ3u1TrmopFxzabrejrXI9H4SHBqhDsUna+27i9QOEhrmLPczFzy7fs0MP9usmyLM37aoOG39RBm/em67Fbe2h7eoY+4sel5VJoaJgkKS83VzMmTdDQESNVMyJCNSMifvZ/biGhYco8fFhjRwzXqVMn9dTkaZKkzMOHFR5eURNe+rsWvjVX/4ufr6H3jVTHLl01c8ok3XnPvVo07z+67a7f69+zXpHd7tDQESMVEhpaaueLi9OmXi3luguUlplVFMiWpEqhIYqNaSu316tjxuUTkuTx+lQ5NFQP9LxBoa4gLVq7STZJ/dq21IrUnSo4sxMtSQU+v3YePqpB7VoraXuaujRrqDU796pXm6ayLEtfbdtd7PEonxwOp2a9MFXfJH2lcRMmXfA9JC83V2EVKhQd2+12+XxeNW3RUr3636rIZs216O23tPCtubpn1ENX3XtIfHy84uPji45jY2MVGxurG2+8UZJUUFCgvLw8RURE6PDhw6pWrZpWrFhxwee95EB2u90aNWrUpY4jQCx4dlZZL+Gys6wfrxAMcxUGbo7bo7Bgl3F7kLLzPZc8l+cp0IsfFP4H+ki/mzQvcb0e7d9dT83/UH/73S1avmWH8gu8v9Yp4hc4mpGhl579q/oOHKxuvXpf8PEfLnpX13fopLvv/4OOZmTob+PHasY/31R4pcrqEFN4/XKHG7powZuFl170vnWQet86SNu3pqpm7TpKTd6ollHXS5JWrVim3wwY+OudHH6RqPq1ZVmWGlavohqVwzUgupXe+2azTuXl658rvlZU/Qj1bNNUHyd/VzTTMbKe0jKPKfG73aoYEqyhXaK1NGWbqlYIVZ+o5nLa7apWsYJ6tW6q5Vt3atPedG3am646VSrpRE6eGlxbRQeOnZAktapTS5v2pZfV6eMijHniaR3POqanxzyol+f857zRGhoWprzcH7+xsixLDodTnW7spgrhhZs7nW7spn/PekXS1fce8kMQ/9SqVaskSY8//rjGjRuniIgIHTlyRM8//3yJnpdrkIGf2HkoU20bFn5oonPTBtq8N13bDmYoqn5tuZwOVQh2qUH1qkrLOPaL5xrVqCq316v046cUHOSUJUt2u11BZ66HRvly4niWJj85Tr+/f5R69htQopnwihWLdn/CK1aUz+uT3+dXizbXaeM3X0uSvt2yWXUbNCo2tyThXd065E658/Nlt9tlk035eXmX94RwWc1P2qgFq5O1YHWyMk5m66Pkb9X3+uaqUqEwfjxen4p9Qk+Fl1i5C3xnvi6Qw27TkZOnNOfLb7RgdbI+2LBVx07naPnW4p9P6BhZX+t271eQwyG/ZcmSeN+4Aqz8/FO9P3+eJCk4OEQ2u012x/lTrEXr67TxzO7yjm+3qn6jwveKyU+O185thd9spW7coMZNmxWb4z2k0IEDBxQRUXjNfs2aNXXo0KESzXENMvATsz9L0uMDb1aQw6G9R7O08ttd8luWFq3dpFdH3C6bzaZ/Lf9aHq9PDapX0W2dojTzo5UXNfeDu7t10MyPVkqSPknZptdH3qHt6Rk6ncflS+XRe/PnKft0thLm/VcJ8/4rSXr6+RcVHBx81mNfmzZFQ0fcrwFD7tQbL72gZ8aOkdfr1bD/u18hoaG6d/RDemPGi/psyQcKq1BBjz49oWg26Yvl6hDTRcEhIYrp3kMvT/6bbDabxv51YqmdKy6PtTv3qn90S/n8lrw+n5ambJMkDYhuqcRtu7Vu1371b9tCd3VtJ4fdppXfXfgyiZa1a+j7I0fl9fm1PT1Dgzq0lmVJizeklsYp4RfofONNmv3SNE3408Pyer0a8eDDcrnOfv+QfnwP6XRjN23euF5/eeQhWZalP45/UpL0wKOPac5rM+V0BumaqlU16k+PF83yHvKjyMhIjR8/XlFRUUpJSVH79u1LNGezzJ8LX4S4uDgusYB6BOAlFrh8Xh15R1kvAeXY0mSutcf59YtuVdZLQDkXVa/Wee/3+/1KTEzUzp071bhxY/Xq1atEz8sOMgAAAAKS3W5Xjx491KNHj4uaI5ABAAAQUH74kN65/PAbLs6HQAYAAEBAeffdd5WamqrOnTufdR+BDAAAgKvOyy+/rOHDh+uBBx5Q48YX/4dS+DVvAAAACCgOh0MvvPCCCgoKLmmeQAYAAEDAqVevnpo3b37O+955553zzhLIAAAAuKp8/PHH572fQAYAAMBV5UJ/BoRABgAAwFXFZrOd934CGQAAADAQyAAAALiqcIkFAAAArkqzZ88udjxjxgxJ0vjx4887xx8KAQAAQEBZuHChEhIStGvXLiUmJkqSfD6fvF6vxo0bp6ioqPPOE8gAAAAIKIMHD1ZMTIzi4uI0evRoSZLdble1atVKNM8lFgAAAAgoLpdLdevW1cSJE7V69WotWrRI+/btU3Z2donmCWQAAAAEpIkTJyo9PV1JSUnKycnRE088UaI5AhkAAAABad++fXr00UcVHBysnj176vTp0yWaI5ABAAAQkHw+n7KysiRJ2dnZsttLlr58SA8AAAABaezYsRo2bJgyMzMVGxurp59+ukRzBDIAAAACUqdOnfTpp58qKytLVatWLfEcgQwAAICA1KdPH/l8vqJjp9OpiIgIjR8/Xq1bt/7ZOQIZAAAAAemGG27QLbfcog4dOig5OVkLFy7UkCFDNHnyZC1YsOBn5/iQHgAAAAJSWlqaunTpIpfLpc6dOyszM1MxMTEX/LAeO8gAAAAISC6XSwsWLFB0dLSSk5PlcrmUmppa7LKLc2EHGQAAAAFp+vTp2rNnj6ZPn679+/frxRdf1LFjxzRlypTzzrGDDAAAgIA0efJkzZgxo9ht3bt3v+AcO8gAAAAISB6PR9u2bZPb7ZbH45HH4ynRHDvIAAAACEh79uzRQw89VHRss9m0fPnyC84RyAAAAAhIS5YsuaQ5AhkAAAABafny5Zo/f74KCgpkWZZOnDhRomjmGmQAAAAEpNdff11jxoxRRESEbrvtNjVr1qxEcwQyAAAAAlKVKlUUHR0tSbr99tt15MiREs0RyAAAAAhIQUFBWrdunbxer7766itlZmaWaI5ABgAAQECKioqS1+vVgw8+qHffffeCf2L6B3xIDwAAAAFl4cKFSkhI0K5du9SkSRNJks/nU0hISInmCWQAAAAElMGDBysmJkZxcXEaPXq0JMlut6tatWolmieQAQAAEFBcLpfq1q2r55577pLmuQYZAAAAMBDIAAAAgIFABgAAAAwEMgAAAGAgkAEAAAADgQwAAAAYCGQAAADAQCADAAAABgIZAAAAMBDIAAAAgIFABgAAAAwEMgAAAGAgkAEAAAADgQwAAAAYCGQAAADAQCADAAAABgIZAAAAMBDIAAAAgIFABgAAAAwEMgAAAGAgkAEAAAADgQwAAAAYCGQAAADAQCADAAAABgIZAAAAMBDIAAAAgMFZ1gvAle3VkXeU9RJQjj0yJ6Gsl4ByrF+7VmW9BJRzvIfgQr58dsyv8rzsIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMDgLOsFAOWF1+vV7OnTlHn4sAoKCjTk7nvUsUtXSdLc2bNUu1499Rk4+KyZWS9MVeaRw7Lb7Rr92HjVqd9AJ48f1z/+/pJysk/L7/drzBNPq1btOop7ebr27tqlvoMGq3ufW5STna05r83UI0/9tSxOGRehZZ2aGtW7i8bOfV91qlbWk7/tJcuS0jKOaebHK2VZ0oB2rTSoQxv5/H69lbhea3bsKfYcFzNXvVIFTbzzFvn8fj2X8JmOns5R76hm8vktrUjdWTb/EFAiYa4g3du9o+LXpMhpt6tvVHP5LUtZOblamrLtnDMR11RSj1aRWrA6WZI0qH1rVQh2SZIqh4Uo/fgpLd6wVX2jmqtGpXBt3HNQWw8clsvpUJ+o5vpw47eldn74Ze66sb26Nm8kp8OuD9Zt0Y5DmXrs1h7y+f3af+yEXlq8Qpb14+NtNulPA3oosua1KvD59NLiFTqYdVJNI6pr6rABOph1UpL0wbot+mLr93rs1h5qUuta/W/dFn22absqBLs0dkB3TXnv8zI64ysTO8jAGV8t+0wVK1XWczNn6S9TX9Sc12bq5IkTmvLUeK1fk3TOmeS1X8vv82nKq7N1x/B7teDf/5IkvfXPf6hbr99o0suvaeiIkTq4b59Onzypk8ePa/Krr2vFJ0slSe8veFu/HXpXqZ0jLs3QrtEaP6inXE6HJOmhvjdqzoq1euTN92Sz2dS1eWNVDQ/TkM7Xa8ycBI1/a7Ee6BWjIEfxt9iLmevRuqneSdqohWs2qUfrJnI5HerSvBFxXM7ZbTb1vb6FvD6/JKlr80ZK2rFHbydtlMNuV2TNamfNdGpSX/3atpDT/uPrZfGGrVqwOlnvrdui/AKvlqfuVEiQU2HBLr21aoOi6kdIkmKaNtTXO/eWzsnhF2vbsI7a1KulMf9O0Ni576tG5Yq6t3tH/WflOj387/cU5HDohqYNi83c2KKxXE6H/jgnQf9v2Wo92Kdw46ZZRHUtXJOisXPf19i57+uLrd+rUmiIqoaH6Y9zEtQ/upUk6e5u7TV/1YbSPtUrHjvIwBk3dO+hG27qUXTscDiUn5en390zQsnfrD3nTETduvL5ffL7/crLyZXDWfif1PatW9SgcWNNGv+YqteqpREPPSypcMe5wONRkMulI4cOyZ2fp/qNGv/q54ZfJj3rlJ6J/1h/ub23pML/MaXsOShJWrtzrzpE1pPf8it1/yEV+Pwq8Hl0MOuEGte8VtvTM4qe52Lm8jwFCgkKks0m5Rd4dWdMWy36elPpnzwuys2tmyhlz0Hd0LSBJOnIydMKcRW+L7icDvn91lkzJ3Ly9P66Lbr1TNCYbmzeSBvTDijH7VGQwy6H3Sanwy6v36/KYSEKcth19HTOr3tSuGw6RtbX7oxjei62vyoEu/SPz1fLb1mqFBosSQoLdsnn9xebua5+bX3z/T5J0rcHjqh57RqSCt9P6l1bRV1bNNKBYyc165Ov5PF65bDb5XI65fF6VeuaigoJClJaRlbpnmgAIJCBM0JDwyRJebm5mjFpgi7taEYAACAASURBVIaOGKmaERGqGRHxs4EcEhqmzMOHNXbEcJ06dVJPTZ4mSco8fFjh4RU14aW/a+Fbc/W/+Pkaet9IdezSVTOnTNKd99yrRfP+o9vu+r3+PesV2e0ODR0xUiGhoaV2vii5xO92qdY1FYuObTZb0de5Ho/CQ4JVIdil7Hy3cXuBwkNcxZ7nYuaWb9mhh/t1k2VZmvfVBg2/qYM2703XY7f20Pb0DH3Ej9TLnTb1ainXXaC0zKyiQD6ek6fe1zVTl6YN5fZ6te/YibPmdhzKVKXQkLNuD3MFqeG1VYp+alDg82vn4aMa1K61kranqUuzhlqzc696tWkqy7L01bbdKvD5z3oelB+Vw0JU85qKemr+h4q4ppKmDBuguV9+o7EDumv4TR2Vne8u+ib6BxWCg4q9R/gtSw67Td8dLHwf2HEoU7/v1l739eikNz5L0urtaXpmSB/N/XKd7uneUW9/tUEP9+smv9/SnBVfK7/AW9qnXSaeeuqpn73v+eefv+A8gXzG3Llz5Xa7L/xAFBPTf/CFH3QFOZqRoZee/av6Dhysbr16X/DxHy56V9d36KS77/+DjmZk6G/jx2rGP99UeKXK6hBT+GOwDjd00YI3Cy+96H3rIPW+dZC2b01Vzdp1lJq8US2jrpckrVqxTL8ZMPDXOzlcNpZxgWCYqzBwc9wehQW7jNuDlJ3vueS5PE+BXvxghSTpkX43aV7iej3av7uemv+h/va7W7R8y46r5n90V4qo+rVlWZYaVq+iGpXDNSC6lWpWDtfclet09HSOohvWUc/WTfT5lh0ler7mtWvo24NHZO45b9qbrk1701WnSiWdyMlTg2ur6MCZ6G5Vp5Y27Uv/Fc4Ml8upvHztO3pcXl/h9cYer09/HdJH/zd7gfZkZum3Ha/Tg3266pWPE4tmctwFxd4j7DabfH5Lq7btKnqP+Wrbbj3S7yZJ0pINW7Vkw1a1rldL6Vkn1a5RXW3eW/i66HVds4D75jo+Pl7x8fFFx7GxsYqNjVX//v0lSQsWLFB0dLTatWunLVu2aMuWLSV6XgL5DLfbrVGjRpX1Mq44m/cfLuslXDYnjmdp8pPjNHLMWF3Xrn2JZsIrVpTD4Sz62uf1ye/zq0Wb67Txm6/VvXdffbtls+o2aFRsbknCu3r4iaf1+YeLVemaa2T5LeXn5V32c8KvY+ehTLVtWEcpew6qc9MGSk47oG0HM3R/zxi5nA4FORxqUL2q0jKO/eK5RjWqyu31Kv34KQUHOWXJkt1uV5DTQSCXM/OTNhZ9PaxLtD7dvF23d7xO7jP/nrLz3apbtXKJn69B9SpnfdDzBx0j6+vD5G/VtkEd5Voe2Ww2BZ25Rh7l15Z9hzSkc5TeXZOiahUrKNTlVHrWSeW4C0P36OkctTlzffkPUvcdUpfmDfXl1u/Vqm5N7T5S+P7w4u8H6dWlidp2MEPtGtXTjkOZxeZ+F9NWU99fpoHtW8uXY8lusynUFVQ6J1qKfgjin+rWrZsk6c0339QDDzwgSWrfvr1GjBhRouclkIEz3ps/T9mns5Uw779KmPdfSdLTz7+o4ODgsx772rQpGjrifg0YcqfeeOkFPTN2jLxer4b93/0KCQ3VvaMf0hszXtRnSz5QWIUKevTpCUWzSV8sV4eYLgoOCVFM9x56efLfZLPZNPavE0vtXPHLzP4sSY8PvFlBDof2Hs3Sym93yW9ZWrR2k14dcbtsNpv+tfxrebw+NaheRbd1itLMj1Ze1NwP7u7WQTM/WilJ+iRlm14feYe2p2fodB4/8boSLN20TYM6tJbfb8lvWUW/xWJAdEslbtt93n+P1SqE6URO/lm3t6xdQ98fOSqvz6/t6Rka1KG1LEtavCH1VzsPXB5rduxRVIPa+scDd8pms2nmR4nKLyjQhDv6yuf3y+vzafqSLyRJT932G81Z8bW+2rZLHSLradbIIbLJphc+WCZJevmjlXq0/03y+vzKys4pmpOknm2aavX2PXIXeLXy2+814Y5bZFmWJiV8WibnXZZyc3O1Zs0aXXfddUpOTlZBQUGJ5myW+TO/ixAXFxdQO66Bdj6lJZB2kHH5PTInoayXgHKsX7uzP5QGmJYG2OUAuPy+fHbMee/ftWuXXnnlFX3//feKjIzUhAkTVL169Qs+LzvIAAAACEiRkZF69dVXL3qO34MMAAAAGNhBBgAAQEAZOHCgjh8/fs77Vq1adcF5AhkAAAABZdasWXrsscf09ttvKyTk7N8zfiFcYgEAAICA0qBBA91zzz1au/bcf+jrQthBBgAAQMAZPPjS/5gZO8gAAAAIKG63W/PmzdPChQvl8fz4V03feeedEs0TyAAAAAgof/7zn5WRkaG0tDTdddddOnnypCTp448/LtE8l1gAAAAgoGRlZemVV16RJH3++ed68MEHNXfuXJX07+MRyAAAAAgoBQUFysrKUtWqVdW7d28dPHhQjz/+eIn/1DSXWAAAACCgPProo7r77rt19OhRSdJ9992nli1bKjU1tUTzBDIAAAACSkxMjJYuXaprr7226LYHH3xQK1eulHThD+sRyAAAALgqVKtWTdKFP6xHIAMAAOCqcqEP6xHIAAAAuKrYbLbz3k8gAwAAAAYCGQAAAFcVLrEAAADAVWn27NnFjmfMmCFJGj9+/Hnn+EMhAAAACCgLFy5UQkKCdu3apcTEREmSz+eT1+vVuHHjFBUVdd55AhkAAAABZfDgwYqJiVFcXJxGjx4tSbLb7UW/5u1CuMQCAAAAAcXlcqlu3bqaOHGiVq9erUWLFmnfvn3Kzs4u0TyBDAAAgIA0ceJEpaenKykpSTk5OXriiSdKNEcgAwAAICDt27dPjz76qIKDg9WzZ0+dPn26RHMEMgAAAAKSz+dTVlaWJCk7O1t2e8nSlw/pAQAAICCNHTtWw4YNU2ZmpmJjY/X000+XaI5ABgAAQEDq1KmTPv30U2VlZalq1aolniOQAQAAEJD69Okjn89XdOx0OhUREaHx48erdevWPztHIAMAACAg3XDDDbrlllvUoUMHJScna+HChRoyZIgmT56sBQsW/OwcH9IDAABAQEpLS1OXLl3kcrnUuXNnZWZmKiYm5oIf1mMHGQAAAAHJ5XJpwYIFio6OVnJyslwul1JTU4tddnEu7CADAAAgIE2fPl179uzR9OnTtX//fr344os6duyYpkyZct45dpABAAAQkCZPnqwZM2YUu6179+4XnGMHGQAAAAHJ4/Fo27Ztcrvd8ng88ng8JZpjBxkAAAABac+ePXrooYeKjm02m5YvX37BOQIZAAAAAWnJkiWXNEcgAwAAICAtX75c8+fPV0FBgSzL0okTJ0oUzVyDDAAAgID0+uuva8yYMYqIiNBtt92mZs2alWiOQAYAAEBAqlKliqKjoyVJt99+u44cOVKiOQIZAAAAASkoKEjr1q2T1+vVV199pczMzBLNEcgAAAAISFFRUfJ6vXrwwQf17rvvXvBPTP+AD+kBAAAgoCxcuFAJCQnatWuXmjRpIkny+XwKCQkp0TyBDAAAgIAyePBgxcTEKC4uTqNHj5Yk2e12VatWrUTzBDIAAAACisvlUt26dfXcc89d0jzXIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADM6yXgCubEuTvy3rJaAc69euVVkvAeXY0o28fwAon9hBBgAAAAwEMgAAAGAgkAEAAAADgQwAAAAYCGQAAADAQCADAAAABgIZAAAAMBDIAAAAgIFABgAAAAwEMgAAAGAgkAEAAAADgQwAAAAYCGQAAADAQCADAAAABgIZAAAAMBDIAAAAgIFABgAAAAwEMgAAAGAgkAEAAAADgQwAAAAYCGQAAADAQCADAAAABgIZAAAAMBDIAAAAgIFABgAAAAwEMgAAAGAgkAEAAAADgQwAAAAYCGQAAADAQCADAAAABgIZAAAAMBDIAAAAgIFABgAAAAwEMgAAAGBwXupgcHCw4uLiLudaylR6enpZLwEAAADlwCUH8n333XcZl1H2Ain2AQAAcOm4xAIAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYHCW9QKA8ibMFaR7u3dU/JoU2STdcn0LySZlnMzWsi07ZBmPtdtsGhDdUpXDQuW3LH2yaZuysnOL7m9Zp6baN6qreas2SJL6RjVXjUrh2rjnoLYeOCyX06E+Uc314cZvS/ck8YuYrxGn3a6+Uc3ltyxl5eRqacq2c85EXFNJPVpFasHqZEnSoPatVSHYJUmqHBai9OOntHjDVl4jV6iWdWpqVO8uGjv3fdWpWllP/raXLEtKyzimmR+vlGVJA9q10qAObeTz+/VW4nqt2bGn2HNczFz1ShU08c5b5PP79VzCZzp6Oke9o5rJ57e0InVn2fxDQIncdWN7dW3eSE6HXR+s26Kdh4/qsVt7qMDr0/eHj+q1TxJlWeef+Tj5OzWNqH7Oucdu7aEmta7V/9Zt0WebtqtCsEtjB3TXlPc+L5sTvkKxgwwY7Dab+l7fQl6fX5J0U8tIrfxut95etVFBDoea1Lq22OMb16wmu82meas2aPWONN3UonHRfTUqhSuqfoRsZ45DgpwKC3bprVUbFFU/QpIU07Shvt65t1TODZfHT18jXZs3UtKOPXo7aaMcdrsia1Y7a6ZTk/rq17aFnPYf33IXb9iqBauT9d66Lcov8Gp56k5eI1eooV2jNX5QT7mcDknSQ31v1JwVa/XIm+/JZrOpa/PGqhoepiGdr9eYOQka/9ZiPdArRkGO4v8Lvpi5Hq2b6p2kjVq4ZpN6tG4il9OhLs0bEcflXNuGddSmXi2N+XeCxs59XzUqV9TjA2/WrE++0iNvvqcct1u/ua7ZBWcknXOuUmiIqoaH6Y9zEtQ/upUk6e5u7TX/zCYNSo5ABgw3t26ilD0HlZ3vliT9b90WHcg6IbvNpgohLuW6PcUefzw7VzZ7YQK7nE75z3zbHxLkVPdWkVpu/M/K5/fLYbfJ6bDL6/ercliIghx2HT2dU0pnh8vhp6+RIydPK8RV+MM4l9Mhv986a+ZETp7eX7flnM93Y/NG2ph2QDluD6+RK1R61ik9E/9x0XGziOpK2XNQkrR25161b1xXLerUUOr+Qyrw+ZXj9uhg1gk1rln8G+6LmcvzFCgkKEihLqfyC7y6M6atFn29qfROGpekY2R97c44pudi+2vqsAFFPw3Yuv+wJGnLvkO6rn7tC85IOuecx+uVw26Xy+mUx+tVrWsqKiQoSGkZWaV6noGAQAbOaFOvlnLdBUrL/PGNxJJUKTREI2/urFBXkI4Zl09IksfrU+XQUD3Q8wbdcn0Lbdi9XzZJ/dq21IrUnfJ4fUWPLfD5tfPwUQ1q11pJ29PUpVlDrU87oF5tmqpn6yZn7Sah/DnXa+R4Tp5+06aZ7r+5syoEu7Tv2Imz5nYcypTvHOEc5gpSw2uraMu+Q5J4jVypEr/bJZ/fX3Rss9mKvs71eBQeEqwKwa6ib6oKby9QeIir2PNczNzyLTvUrnFdtW1YRxt271edqpVls9n02K09NKBdq1/jNHEZVA4LUfPaNfTswk/09w+/1F9u761Dx0/r+gaFUdyleSOFBDkvOCNJ6cdPnTWXX+DV6u1pemZIH839cp3u6d5Ri9Zu0sP9uumPfW8867nx8/gndZnNnTtXbrf7wg8MFBFNy3oFl01U/dqyLEsNq1dRjcrhGhDdSu99s1mn8vL1zxVfK6p+hHq2aaqPk78rmukYWU9pmceU+N1uVQwJ1tAu0Vqask1VK4SqT1RzOe12VatYQb1aN9XyrTu1aW+6Nu1NV50qlXQiJ08Nrq2iA2eCqlWdWtq0L72sTh8lcK7XSM3K4Zq7cp2Ons5RdMM66tm6iT7fsqNEz9e8dg19e/BIsevaeY1c+SzjAtIwV2Hg5rg9Cgt2GbcHKTvfc8lzeZ4CvfjBCknSI/1u0rzE9Xq0f3c9Nf9D/e13t2j5lh3KL/D+WqeIS3QqL1/7jh6X1+fX/mMn5PH6NOuTrzTi5k4a2rWdtqdnqMDYWPm5mWsqhOqF/y3Xw/26nTW3ZMNWLdmwVa3r1VJ61km1a1RXm/cWvm/0uq6ZPgqwzzPEx8crPj6+6Dg2NlaxsbG68cYbJUkFBQXKy8tTRESEDh8+rGrVqmnFihUXfF4C+TJzu90aNWpUWS+j1Lyw+MIvsivF/KSNRV8P6xKtTzdvV9/rm+uLrd/reE5e4W7wTzYB8wu8RTuD+QUFcthtOnLylOZ8+Y2kwt3nwe1ba/nW4tcFdoysrw+Tv1XbBnWUa3lks9kUdOb6RZRf53qN3N7xOrnPhEh2vlt1q1Yu8fM1qF7lrA9q/YDXyJVr56FMtW1YRyl7Dqpz0wZKTjugbQczdH/PGLmcDgU5HGpQvarSMo794rlGNarK7fUq/fgpBQc5ZcmS3W5XkNNBIJdDW/Yd0pDOUXp3TYqqVaygUJdT19WP0AsfrNCx0zl6pN9NWvv93gvOnMrNV++o5ued+11MW019f5kGtm8tX44lu82mUFdQaZ5uqfghiH9q1apVkqTHH39c48aNU0REhI4cOaLnn3++RM9LIAPnsXbnXvWPbimf35LX5yv6DQUDolsqcdturdu1X/3bttBdXdvJYbdp5Xe7VeDzn/c5W9auoe+PHJXX59f29AwN6tBaliUt3pBaGqeEy2zppm0a1KG1/H5Lfss66zVyOu/nf6JUrUKYTuTkn3U7r5Er2+zPkvT4wJsV5HBo79Esrfx2l/yWpUVrN+nVEbfLZrPpX8u/lsfrU4PqVXRbpyjN/GjlRc394O5uHTTzo5WSpE9Stun1kXdoe3rGeV93KDtrduxRVIPa+scDd8pms2nmR4lyOux64e5blV/gVXLaQa0986Hcp277jeas+PqcM37L0oFjJ845J0k92zTV6u175C7wauW332vCHbfIsixNSvi0rE69zBw4cEAREYUfeq5Zs6YOHTpUojmbZf30l4lcneLi4i7Lzu/lep4rRSDtIAMoXUsD7Ee9AErfl8+OOe/9f/nLX+TxeBQVFaWUlBTVrFlTf/7zny/4vOwgAwAAICA999xzSkxM1Pfff68BAwaoZ8+eJZrjI9EAAAAIKOvXry/6+uDBg0pLS9ORI0fk8/nOM/UjAhkAAAAB5dVXX5UkvfTSS9q+fbv69Omjffv2afLkySWa5xILAAAABKTNmzfr7bffliR1795dw4cPL9EcO8gAAAAIKIcOHdLnn3+uihUr6sCBA5KkI0eOKD//7N8cdC4EMgAAAALKn//8Z23dulU+n0/Lli3T6dOnNXToUP3pT38q0TyXWAAAACCg9O7dW7179y522xdffFH09TvvvKOhQ4f+7Dw7yAAAALiqfPzxx+e9n0AGAADAVeVCfyePQAYAAMBVxWaznfd+AhkAAAAwEMgAAAC4qnCJBQAAAK5Ks2fPLnY8Y8YMSdL48ePPO8eveQMAAEBAWbhwoRISErRr1y4lJiZKknw+n7xer8aNG6eoqKjzzhPIAAAACCiDBw9WTEyM4uLiNHr0aEmS3W5XtWrVSjTPJRYAAAAIKC6XS3Xr1tXEiRO1evVqLVq0SPv27VN2dnaJ5glkAAAABKSJEycqPT1dSUlJysnJ0RNPPFGiOQIZAAAAAWnfvn169NFHFRwcrJ49e+r06dMlmiOQAQAAEJB8Pp+ysrIkSdnZ2fr/7d19XJX1/cfx9znAAcU7MMujUKmpTZINIwjvNXVqpjMzyrLpNjc1vCuZaaVluJY3mUtUfsuy4c0UWjVXqU03FW/SiFIo70jUxBRFTVE4d9fvD4tdFMkRTfD4ev7l4ZzPxXX5+Hadl1fXAavVu/TlQ3oAAADwSWPHjtXDDz+sgoICxcfHa9KkSV7NEcgAAADwSTExMVq9erUKCwsVGhrq9RyBDAAAAJ/Uo0cPud3u0sf+/v6y2+1KTExURETEj84RyAAAAPBJd999t3r27Kno6GhlZWUpLS1NAwYMUFJSkpYtW/ajc3xIDwAAAD5p//79atu2rWw2m2JjY1VQUKC4uLgKP6zHFWQAAAD4JJvNpmXLlikqKkpZWVmy2WzKzs4uc9tFebiCDAAAAJ80c+ZM5eXlaebMmTp06JCmT5+uEydOaNq0aRed4woyAAAAfFJSUpJmzZpV5mudOnWqcI4ryAAAAPBJDodDu3btUklJiRwOhxwOh1dzXEEGAACAT8rLy9PIkSNLH1ssFq1du7bCOQIZAAAAPmnlypWVmiOQAQAA4JPWrl2rpUuXyul0yjAMnTp1yqto5h5kAAAA+KTk5GQlJCTIbrerf//+atGihVdzBDIAAAB8UkhIiKKioiRJ999/v44ePerVHIEMAAAAnxQQEKDt27fL5XJp48aNKigo8GqOQAYAAIBPioyMlMvl0ogRI7RixYoKf8X0d/iQHgAAAHxKWlqa0tPTlZubq9tuu02S5Ha7FRQU5NU8gQwAAACf0q9fP8XFxSklJUXDhw+XJFmtVtWvX9+reQIZAAAAPsVmsyksLEwvvPBCpea5BxkAAAAwIZABAAAAEwIZAAAAMCGQAQAAABMCGQAAADAhkAEAAAATAhkAAAAwIZABAAAAEwIZAAAAMCGQAQAAABMCGQAAADAhkAEAAAATAhkAAAAwIZABAAAAEwIZAAAAMCGQAQAAABMCGQAAADAhkAEAAAATAhkAAAAwIZABAAAAEwIZAAAAMCGQAQAAABMCGQAAADAhkAEAAAATAhkAAAAwIZABAAAAE/+q3oHqIjAwUCkpKZe9nfz8/CuwN9eOXlGtqnoXUI2NXphe1bsAAMAlI5C/NWTIkCuynSsR2QAAAKg63GIBAAAAmBDIAAAAgAmBDAAAAJgQyAAAAIAJgQwAAACYEMgAAACACYEMAAAAmBDIAAAAgAmBDAAAAJgQyAAAAIAJgQwAAACYEMgAAACACYEMAAAAmBDIAAAAgAmBDAAAAJgQyAAAAIAJgQwAAACYEMgAAACACYEMAAAAmBDIAAAAgAmBDAAAAJgQyAAAAIAJgQwAAACYEMgAAACACYEMAAAAmBDIAAAAgAmBDAAAAJgQyAAAAIAJgQwAAACYEMgAAACACYEMAAAAmBDIAAAAgAmBDAAAAJgQyAAAAIAJgQwAAACYEMgAAACACYEMAAAAmBDIAAAAgAmBDAAAAJgQyAAAAIAJgQwAAACYEMgAAACACYEMAAAAmBDIAAAAgAmBDAAAAJgQyAAAAIAJgQwAAACYEMgAAACACYEMAAAAmBDIAAAAgAmBDAAAAJgQyAAAAIAJgQwAAACYEMgAAACACYEMAAAAmBDIAAAAgAmBDAAAAJgQyAAAAIAJgQwAAACYEMgAAACACYEMAAAAmBDIAAAAgAmBDAAAAJj4V/UOANWF2+1WysszlP/VIVmtVo1MfEoNGzWWJC2aN1eNwsPV475+ZWY8Ho9e+8ts5eXuU0CATcOfTJS9cZi+3LtHf35mouyNwyRJPe7rp3Zduipl9kwdyM3VL/v2U6cePVV09qwWvvqKRk985qofLypnUPs71a5lE/n7WfXu9p3ac6RAT/TpLLfHo0MnTmnGP9fJMP73eotFGndvZzW76QY53W7N+Oc6HS48reb2BvrTw/fqcOFpSdK723fqPzn79ESfzrqt4Q16Z/tOrflst4IDbRp7bydN+8eHVXTEuBTfXx97vz6uJ/p0ltPl1r6vj+vVVRvKrI/yZt7P+kLN7Q3KnWN9XLt+1vgm/aF7W41d9LYah9bVU7+6R4Yh7T92Qq+8v16GId3bppX6Rt8ht8ej1A0fa8uevDLbuJS5BnWCNWVgT7k9Hr2QvkbHzxSpe2QLuT2G1mXvrZq/hGsIV5CBb2Vu3SxJSpqTrPhf/0Zvzk/W6VOnNG1ioj7esqncme2bMuRwOPSnV+frkd/9Xn9bME+StH/vHt33wIN6/uU5ev7lOWrXpavOnD6t0ydPKukvyVq36gNJ0tvLluhXDw26OgeIy/aLWxvrjvCGSng9XWMXva0b69bWrzvdpTfXb9eo1/+hAD8/3d381jIz7W9vKpu/nx5fmK7/+/dmjejRTpLUwt5AaVs+1dhFb2vsorf1n5x9qlMjSKG1aurxhenqHdVKkvRIhzu1NCPzah8qKqG89TH+vi6au2qjRr/xDxWVlKhb6xYVzkgqd471ce16qF2UEvt2lc3fT5I08pfttXDdRxr9xj9ksVjUrmVThdaqqQGxP1fCwnQlpv5Tw+6JU4Bf2Uy7lLnOEc31902fKG3LZ+occZts/n5q27IJcewlriAD34pp10F33h0nSSo4dlR1Q0JUfP68HnxsqLK2fVTuzBfZOxR1V4wkqUWrCOXu2S1Jyt2zW/lfHdL2zRlq2DhMQ0eOUoDNJpfLJafDoQCbTUePHFFJ8Xnd3KTp1TlAXLa7mt2sL4+d0AvxvRUcaNOCDzfLYxiqUyNQklQz0Ca3x1NmpvXNjbRt30FJ0udfHVXLRjdKuhDI4TeEqN3tTfTVidOau2qjHC6X/KxW2fz95XC51LBebQUFBGj/scKre6ColPLWR9/oCOUc+lqStPPgEbW/vak+3LHnojOS1KBO8A/mNn7xJevjjBLJtwAAEHlJREFUGpVf+I2eXf6+nr6/u6QL//1/mndYkvTR3gOKbhYuj+FR9qEjcro9crodOlx4Sk1vukG784+VbudS5s47nAoKCJDFIhU7XRoY9wu9tfWzq3/w1ygCGTDx8/PX3Jf+pG2bNurJyVN1k92um+z2Hw3k8+fOqWZwcOljq9Uqt9ul5rf/TPf07qNmLVrqrSWpSktdpMf+MFJ3tW2nV6ZN1cDHfq23Fr+p/oMe1etz58hq9dNDQ3+roBo1rtahohLq1gzSTfVqa+LSf8ler46mPXyvFv13m8be20mDO96ls8UlpW9e3wkODNDZ4pLSxx7DkJ/Voi8OH9N7n3yuPUcK9GiHOzWkc4zmr9mkzbv369kBPbTov9v1WKe7tGRjpkb16iCPx9DCdVtV7HRd7cOGl8pbH0dOntHPb2mkzw7kq23LJgoK8K9w5rG5S5R/8psfzBU7XayPa9SGL3LVsF7t0scWi6X0z+ccDtUKClRwoK3MueKcw6laQbYy27mUubU792hUrw4yDEOLN2ZqcMdo7TiQryf6dNbu/AvnH182ceLEH33uxRdfrHCeQL7CAgMDlZKSUtW7cdXE9e5X8YuuMQkTJulk4QlNShih2QvfvGi01qhZU+fPnSt9bBiG/Pz8FdO+g4JrXTgZxrTvoNfnzpEkde/TV9379NXunGzd1KixsrM+0c8ify5Jylj3b3W7976f8Mhwub45X6yDx0/K5b5wv7HD5dYzA3roN/OWKa+gUL+6q7VG9GinOe9vKJ0pKnGqZuD/3uSsFovcHkMZu3J1ttghSdq460uN7tVRkrQyM0crM3MUEd5Q+YWn1aZJmHYcyJck3dO6hc+/qV3Lylsfc1dt1NAuMXqoXRvtzj8mp8td4Uy94Bp66Z21GtWrww/mWB++wTDdiF7TdiFwi0ocZc4VNW0BpeeIysyddzg1/d11kqTRvTpq8YaPNaZ3J01c+i89/2BPrd25xyf+QbV8+XItX7689HF8fLzi4+PVu3dvSdKyZcsUFRWlNm3aaOfOndq5c6dX2yWQr7AhQ4ZU9S5cVTu+/V+AvmD9h6tVWFCg/oMeVWBgkCxWi6x+F79N//aI1vp462a17dxVez7P0c1NmkiSkp5K1G8Sxqj57T9T9ieZatq87H2HK9NXaNSESfrwX/9UnXr1ZHgMFZ8//5MdG66MnQePaEBspFZs+VT1awerhs1f+YWnVVRy4U3s+Jki3XGzvcxM9sEjatvyVv03Z59ahd2kL4+ekCRNf7Sv/vLBBu06fExtmoRrz5GCMnMPxv1Cf3r737rvzgi5iwxZLRbVsAVcnQNFpZS3PlrfbNdL767TiTNFGt2roz7ad6DCmW/OFat7ZMuLzrE+rm17jxToF7c21qd5hxXb/BZl7f9Kuw4f0++6xsnm76cAPz/d0iBU+4+duOy5JjeGqsTlUv7JbxQY4C9DhqxWqwL8/XwikL8L4u/r0KGDJOmNN97QsGHDJEl33nmnhg4d6tV2CWTgW7HtO2rejD9r8rhRcrlcGjpilGy2wHJf++qfp+mhob9TTPsO2vHJx3p69EgZhqHHE5+SJA0b84QWvvqK/P0DVC80VH8YN750dtN/1io6rq0Cg4IU16mzZic9L4vForHPTLkqx4nK27InT5G3NNKCYQNlsVj0ynsbVOx0avIDv5Tb45HL7dbMlf+RJE3s300L123Vxl25im4Wrrm/HSCLLHrp3X9Lkma/t15jeneUy+1R4dmi0jlJ6npHc23enacSp0vrP9+nyQ/0lGEYmpq+ukqOG94pb334+1n10iN9VOx0KWv/YX2090Lofrc+ypvxGIa+OnGq3DmJ9eEL5q3ZpPH3dVGAn58OHC/U+s9z5TEMvfXRZ/rL0PtlsVj02tqtcrjcuqVBiPrHROqV99Zf0tx3HukQrVfeWy9JWvXpLiX/9gHtzj+mM+dLfmz3fMq5c+e0ZcsWtW7dWllZWXI6nV7NWQzj+z9wBvCeL11BxpU3emF6Ve8CAMCH/fe5hIs+n5ubqzlz5mjfvn1q1qyZJk+erAYNGlS4Xa4gAwAAwCeFhoYqMTFRtWvXVr169byeI5ABAADgU3bs2KGpU6fK4/EoODhYZ8+elWEYmjx5stq0aVPhPIEMAAAAn/Liiy/q1Vdfld3+vw9O5+fna8yYMUpLS6twnt+kBwAAAJ/icrnKxLEk2e32Mj9L+mK4ggwAAACf0qlTJw0ZMkTt2rVT7dq1VVRUpIyMDHXs2NGreQIZAAAAPiUhIUGff/65MjMzdfLkSdWqVUvjx49XRESEV/MEMgAAAHxOq1at1KpVq0rNEsgAAADwKRkZGT/6XPv27SucJ5ABAADgU1asWKHs7GzFxsb+4DkCGQAAANed2bNna/DgwRo2bJiaNm16yfP8mDcAAAD4FD8/P7300ktyOp2VmieQAQAA4HPCw8PVsmXLcp/7+9//ftFZAhkAAADXlffff/+izxPIAAAAuK4YhnHR5wlkAAAAXFcq+pXTBDIAAABgQiADAADgusItFgAAALguzZs3r8zjWbNmSZISExMvOscvCgEAAIBPSUtLU3p6unJzc7VhwwZJktvtlsvl0pNPPqnIyMiLzhPIAAAA8Cn9+vVTXFycUlJSNHz4cEmS1WpV/fr1vZrnFgsAAAD4FJvNprCwME2ZMkWbN2/WW2+9pYMHD+rs2bNezRPIAAAA8ElTpkxRfn6+Nm3apKKiIk2YMMGrOQIZAAAAPungwYMaM2aMAgMD1bVrV505c8arOQIZAAAAPsntdquwsFCSdPbsWVmt3qUvH9IDAACATxo7dqwefvhhFRQUKD4+XpMmTfJqjkAGAACAT4qJidHq1atVWFio0NBQr+cIZAAAAPikHj16yO12lz729/eX3W5XYmKiIiIifnSOQAYAAIBPuvvuu9WzZ09FR0crKytLaWlpGjBggJKSkrRs2bIfneNDegAAAPBJ+/fvV9u2bWWz2RQbG6uCggLFxcVV+GE9riADAADAJ9lsNi1btkxRUVHKysqSzWZTdnZ2mdsuysMVZAAAAPikmTNnKi8vTzNnztShQ4c0ffp0nThxQtOmTbvoHFeQAQAA4JOSkpI0a9asMl/r1KlThXNcQQYAAIBPcjgc2rVrl0pKSuRwOORwOLya4woyAAAAfFJeXp5GjhxZ+thisWjt2rUVzhHIAAAA8EkrV66s1ByBDAAAAJ+0du1aLV26VE6nU4Zh6NSpU15FM/cgAwAAwCclJycrISFBdrtd/fv3V4sWLbyaI5ABAADgk0JCQhQVFSVJuv/++3X06FGv5ghkAAAA+KSAgABt375dLpdLGzduVEFBgVdzBDIAAAB8UmRkpFwul0aMGKEVK1ZU+Cumv8OH9AAAAOBT0tLSlJ6ertzcXN12222SJLfbraCgIK/mCWQAAAD4lH79+ikuLk4pKSkaPny4JMlqtap+/fpezRPIAAAA8Ck2m01hYWF64YUXKjXPPcgAAACACYEMAAAAmBDIAAAAgAmBDAAAAJgQyAAAAIAJgQwAAACYEMgAAACACYEMAAAAmBDIAAAAgAmBDAAAAJgQyAAAAIAJgQwAAACYEMgAAACACYEMAAAAmBDIAAAAgAmBDAAAAJgQyAAAAIAJgQwAAACYEMgAAACACYEMAAAAmBDIAAAAgInFMAyjqncCAAAAqC64ggwAAACYEMgAAACACYEMAAAAmBDIAAAAgAmBDAAAAJgQyAAAAIAJgQwAAACYEMi4bpWUlCgtLa3Ktut2uzV69Ght2LCh3OfbtWt3pXcNXqjKdbFlyxbFx8frkUce0ejRo3X+/PnS5w4cOKA+ffqUO7dhwwY99dRTV3R/UbGqXCtr1qxRt27dNHjwYA0ePFjbtm37wWs4h1x91fF9Ze7cuXrggQf00EMPaceOHT+Y4fxRPgIZ162CgoKf5ETmzXYPHjyoRx99VDt37rzi3x+XpyrXxXPPPafk5GQtWbJEt9xyS+nr33nnHY0bN04nT5684vuFyqvKtZKTk6PExESlpqYqNTVVMTExV3w/cOmq2/tKTk6Otm3bprS0NL388st6/vnnr/i++Sr/qt4BoKosWLBA+/bt09y5c5Wdna2SkhKdOnVKjz/+uLp166Y+ffro1ltvlc1m0zPPPKPx48fL4XCoSZMm2rp1qz788ENt27ZNs2fPlp+fn8LDwzV16tQy201ISCj3e587d05JSUn661//Wvo1t9utZ599Vvv27VN4eLgcDsfV+quASVWui9TUVN1www2SJJfLpcDAQElS3bp1tXjxYnXv3r30tbm5uZo0aZJq1KihGjVqqG7duj/9Xw7KqMq1kpOToy+++EJvvvmmIiMjNX78eFksFs4hVay6va9kZmaqffv2slgsatSokdxutwoLC3Xy5EnOHxUxgOvUoUOHjIEDBxqbNm0ytm7dahiGYWRmZhpDhgwxDMMwunTpYuTk5BiGYRjTpk0zFi9ebBiGYWRkZBhdunQxPB6P0aNHD+P48eOGYRjG7NmzjeXLl5du1xsTJkww1q9fbxiGYaxbt8544oknDMMwjMOHDxsRERFX7mDhteqwLtasWWP079/fKC4uLvP1tm3blv551KhRRkZGhmEYhpGSkmJMmDDhMo4alVGVa+X11183Dh48aHg8HuPZZ581UlNTOYdUA9Xh/GF+X0lOTjaWLFlS+tygQYOMvLw8zh9e4AoyrnsNGjTQ/PnzlZ6eLovFIpfLVfpckyZNJF24Wte/f39JUnR0tCSpsLBQx44d09ixYyVJxcXFl3XP3969exUZGSlJatSokex2e6W3hctXVeti0aJFWrVqlV577bXSK8jlMa+XNm3a6Msvv7y0A8QVUxVrZcCAAapTp44k6Z577tHq1at17tw5ziHVRHV5X6lVq5aKiopKHxcVFal27dqcP7zAPci4blmtVnk8Hs2ZM0f9+vXTjBkzFBsbK8MwyrxGklq0aKGsrCxJ0qeffipJCgkJUcOGDTVv3jylpqZq+PDhio2NLd3upWratGnpto8ePaqjR49e7iGiEqpyXcyfP18ff/yxFi1apNDQ0Iu+tmnTpqXfOzs7u9LHi8qrqrViGIb69u2rr7/+WtKFD3dGRERwDqkGqtv7Sps2bZSRkSGPx6P8/Hx5PB6FhoZy/vACV5Bx3apfv76cTqf27t2radOmKSUlRXa7vdwPQg0bNkx//OMf9cEHH+jGG2+Uv7+/rFarnn76af3+97+XYRgKDg7W9OnTVatWLTmdTs2YMUOJiYle70+3bt2UmZmpgQMHqlGjRgoJCbmShwsvVdW6OH78uJKTk9WqVSsNGzZMktSrVy8NGjSo3P2cMmWKxo0bp4ULFyo0NPSiV5vx06iqtWKxWJSUlKSEhAQFBQWpWbNmevDBBxUQEMA5pIpVt/eVO+64Q9HR0YqPj5fH49HkyZMlcf7whsUw/7MGQLnWr1+vkJAQRUZGavPmzVqwYIH+9re/VfVuoYqxLuAt1gq+jzVRvXEFGfBCWFiYJk2aJD8/P3k8Hj399NMVzuzYsUMzZsz4wdcvdlUQ1xbWBbzFWsH3sSaqN64gAwAAACZ8SA8AAAAwIZABAAAAEwIZAAAAMCGQAQAAABMCGQAAADD5f++W+BkqfyROAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(y.corr(), cmap=sns.diverging_palette(h_neg=20, h_pos=220), center=0, annot=True, fmt='.2%');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:16:05.855151Z",
     "start_time": "2020-06-17T19:16:04.797474Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(X.corr(), cmap=sns.diverging_palette(h_neg=20, h_pos=220), center=0);\n",
    "plt.gcf().set_size_inches((14, 14))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:16:06.329126Z",
     "start_time": "2020-06-17T19:16:05.856465Z"
    }
   },
   "outputs": [],
   "source": [
    "corr_mat = X.corr().stack().reset_index()\n",
    "corr_mat.columns=['var1', 'var2', 'corr']\n",
    "corr_mat = corr_mat[corr_mat.var1!=corr_mat.var2].sort_values(by='corr', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:16:06.336963Z",
     "start_time": "2020-06-17T19:16:06.330037Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var1</th>\n",
       "      <th>var2</th>\n",
       "      <th>corr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>return_42d</td>\n",
       "      <td>return_63d</td>\n",
       "      <td>0.835634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>return_63d</td>\n",
       "      <td>return_42d</td>\n",
       "      <td>0.835634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rsi</td>\n",
       "      <td>macd</td>\n",
       "      <td>0.817113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>macd</td>\n",
       "      <td>rsi</td>\n",
       "      <td>0.817113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>return_21d</td>\n",
       "      <td>macd</td>\n",
       "      <td>0.793893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>return_21d</td>\n",
       "      <td>bb_high</td>\n",
       "      <td>-0.632777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>bb_high</td>\n",
       "      <td>return_10d</td>\n",
       "      <td>-0.693640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>return_10d</td>\n",
       "      <td>bb_high</td>\n",
       "      <td>-0.693640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>bb_high</td>\n",
       "      <td>rsi</td>\n",
       "      <td>-0.696555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rsi</td>\n",
       "      <td>bb_high</td>\n",
       "      <td>-0.696555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           var1        var2      corr\n",
       "514  return_42d  return_63d  0.835634\n",
       "569  return_63d  return_42d  0.835634\n",
       "4           rsi        macd  0.817113\n",
       "224        macd         rsi  0.817113\n",
       "452  return_21d        macd  0.793893\n",
       "449  return_21d     bb_high -0.632777\n",
       "63      bb_high  return_10d -0.693640\n",
       "393  return_10d     bb_high -0.693640\n",
       "56      bb_high         rsi -0.696555\n",
       "1           rsi     bb_high -0.696555"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_mat.head().append(corr_mat.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:16:06.468704Z",
     "start_time": "2020-06-17T19:16:06.338368Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y.boxplot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression for Statistical Inference: OLS with statsmodels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ticker-wise standardization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`statsmodels` warns of high design matrix condition numbers. This can arise when the variables are not standardized and the Eigenvalues differ due to scaling. The following step avoids this warning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:16:12.388379Z",
     "start_time": "2020-06-17T19:16:06.469584Z"
    }
   },
   "outputs": [],
   "source": [
    "sectors = X.iloc[:, -10:]\n",
    "X = (X.drop(sectors.columns, axis=1)\n",
    "     .groupby(level='ticker')\n",
    "     .transform(lambda x: (x - x.mean()) / x.std())\n",
    "    .join(sectors)\n",
    "    .fillna(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-Day Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:16:12.697331Z",
     "start_time": "2020-06-17T19:16:12.389843Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              target_1d   R-squared:                       0.010\n",
      "Model:                            OLS   Adj. R-squared:                  0.009\n",
      "Method:                 Least Squares   F-statistic:                     18.88\n",
      "Date:                Wed, 17 Jun 2020   Prob (F-statistic):          8.58e-184\n",
      "Time:                        15:16:12   Log-Likelihood:             2.8850e+05\n",
      "No. Observations:              109675   AIC:                        -5.769e+05\n",
      "Df Residuals:                  109618   BIC:                        -5.763e+05\n",
      "Df Model:                          56                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=========================================================================================\n",
      "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------\n",
      "const                    -0.0002      0.000     -0.793      0.428      -0.001       0.000\n",
      "rsi                       0.0002      0.000      1.126      0.260      -0.000       0.001\n",
      "bb_high                   0.0002      0.000      0.740      0.459      -0.000       0.001\n",
      "bb_low                    0.0006      0.000      2.560      0.010       0.000       0.001\n",
      "atr                      -0.0002   6.61e-05     -3.275      0.001      -0.000   -8.69e-05\n",
      "macd                     -0.0005      0.000     -1.992      0.046      -0.001   -7.56e-06\n",
      "return_1d                 0.0029      0.000      9.767      0.000       0.002       0.003\n",
      "return_5d                -0.0019      0.001     -2.073      0.038      -0.004      -0.000\n",
      "return_10d               -0.0064      0.001     -6.434      0.000      -0.008      -0.004\n",
      "return_21d                0.0028      0.000      6.200      0.000       0.002       0.004\n",
      "return_42d               -0.0035      0.001     -5.830      0.000      -0.005      -0.002\n",
      "return_63d               -0.0020      0.000     -4.350      0.000      -0.003      -0.001\n",
      "return_1d_lag1            0.0027      0.000      9.087      0.000       0.002       0.003\n",
      "return_5d_lag1            0.0048      0.001      7.047      0.000       0.003       0.006\n",
      "return_10d_lag1          -0.0009      0.001     -0.796      0.426      -0.003       0.001\n",
      "return_21d_lag1           0.0029      0.000      7.577      0.000       0.002       0.004\n",
      "return_1d_lag2            0.0027      0.000      9.269      0.000       0.002       0.003\n",
      "return_5d_lag2            0.0008      0.001      1.054      0.292      -0.001       0.002\n",
      "return_10d_lag2           0.0001      0.001      0.138      0.890      -0.002       0.002\n",
      "return_21d_lag2           0.0003      0.000      0.965      0.334      -0.000       0.001\n",
      "return_1d_lag3            0.0027      0.000      9.067      0.000       0.002       0.003\n",
      "return_5d_lag3            0.0014      0.001      1.821      0.069      -0.000       0.003\n",
      "return_10d_lag3           0.0005      0.000      3.460      0.001       0.000       0.001\n",
      "return_21d_lag3          -0.0002   5.55e-05     -3.795      0.000      -0.000      -0.000\n",
      "return_1d_lag4            0.0030      0.000     10.072      0.000       0.002       0.004\n",
      "return_5d_lag4            0.0006      0.001      1.021      0.307      -0.001       0.002\n",
      "return_10d_lag4           0.0004      0.000      3.831      0.000       0.000       0.001\n",
      "return_21d_lag4        3.547e-05    5.5e-05      0.645      0.519   -7.23e-05       0.000\n",
      "return_1d_lag5        -8.947e-05   6.19e-05     -1.445      0.148      -0.000    3.19e-05\n",
      "return_5d_lag5            0.0001      0.001      0.200      0.842      -0.001       0.001\n",
      "return_10d_lag5           0.0007   9.95e-05      7.223      0.000       0.001       0.001\n",
      "return_21d_lag5         9.29e-05   5.48e-05      1.695      0.090   -1.45e-05       0.000\n",
      "year_2014                -0.0004   8.37e-05     -4.589      0.000      -0.001      -0.000\n",
      "year_2015                -0.0006   9.15e-05     -6.906      0.000      -0.001      -0.000\n",
      "year_2016                -0.0005   8.95e-05     -5.167      0.000      -0.001      -0.000\n",
      "year_2017                -0.0002    8.8e-05     -2.485      0.013      -0.000   -4.62e-05\n",
      "month_2                   0.0010   7.17e-05     13.632      0.000       0.001       0.001\n",
      "month_3                   0.0003   7.43e-05      4.311      0.000       0.000       0.000\n",
      "month_4                   0.0005   7.35e-05      6.813      0.000       0.000       0.001\n",
      "month_5                   0.0005   7.25e-05      6.555      0.000       0.000       0.001\n",
      "month_6                   0.0004   7.34e-05      5.552      0.000       0.000       0.001\n",
      "month_7                   0.0006   7.62e-05      8.234      0.000       0.000       0.001\n",
      "month_8                6.735e-05    7.7e-05      0.875      0.382   -8.35e-05       0.000\n",
      "month_9                   0.0004   7.59e-05      5.129      0.000       0.000       0.001\n",
      "month_10                  0.0007    7.8e-05      8.422      0.000       0.001       0.001\n",
      "month_11                  0.0006   7.59e-05      7.431      0.000       0.000       0.001\n",
      "month_12                  0.0004   7.41e-05      4.884      0.000       0.000       0.001\n",
      "capital_goods             0.0010      0.000      2.714      0.007       0.000       0.002\n",
      "consumer_non-durables     0.0007      0.000      1.793      0.073   -6.34e-05       0.001\n",
      "consumer_services         0.0008      0.000      2.234      0.026    9.29e-05       0.001\n",
      "energy                    0.0003      0.000      0.913      0.361      -0.000       0.001\n",
      "finance                   0.0009      0.000      2.533      0.011       0.000       0.002\n",
      "health_care               0.0007      0.000      2.065      0.039    3.51e-05       0.001\n",
      "miscellaneous             0.0011      0.000      2.564      0.010       0.000       0.002\n",
      "public_utilities          0.0003      0.000      0.749      0.454      -0.001       0.001\n",
      "technology                0.0011      0.000      3.284      0.001       0.000       0.002\n",
      "transportation            0.0010      0.000      2.426      0.015       0.000       0.002\n",
      "==============================================================================\n",
      "Omnibus:                    29135.691   Durbin-Watson:                   2.010\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          2803353.255\n",
      "Skew:                          -0.103   Prob(JB):                         0.00\n",
      "Kurtosis:                      27.767   Cond. No.                         80.6\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "target = 'target_1d'\n",
    "model = OLS(endog=y[target], exog=add_constant(X))\n",
    "trained_model = model.fit()\n",
    "print(trained_model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5-Day Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:17:33.116373Z",
     "start_time": "2020-06-17T19:17:32.871109Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              target_5d   R-squared:                       0.030\n",
      "Model:                            OLS   Adj. R-squared:                  0.030\n",
      "Method:                 Least Squares   F-statistic:                     61.54\n",
      "Date:                Wed, 17 Jun 2020   Prob (F-statistic):               0.00\n",
      "Time:                        15:17:33   Log-Likelihood:             3.7879e+05\n",
      "No. Observations:              109675   AIC:                        -7.575e+05\n",
      "Df Residuals:                  109618   BIC:                        -7.569e+05\n",
      "Df Model:                          56                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=========================================================================================\n",
      "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------\n",
      "const                    -0.0005      0.000     -3.410      0.001      -0.001      -0.000\n",
      "rsi                       0.0005   8.43e-05      5.725      0.000       0.000       0.001\n",
      "bb_high                   0.0010   9.88e-05     10.040      0.000       0.001       0.001\n",
      "bb_low                   -0.0005   9.44e-05     -5.215      0.000      -0.001      -0.000\n",
      "atr                      -0.0002    2.9e-05     -7.942      0.000      -0.000      -0.000\n",
      "macd                     -0.0004      0.000     -4.143      0.000      -0.001      -0.000\n",
      "return_1d                 0.0010      0.000      7.605      0.000       0.001       0.001\n",
      "return_5d                 0.0012      0.000      2.848      0.004       0.000       0.002\n",
      "return_10d               -0.0038      0.000     -8.624      0.000      -0.005      -0.003\n",
      "return_21d                0.0021      0.000     10.641      0.000       0.002       0.003\n",
      "return_42d               -0.0023      0.000     -8.844      0.000      -0.003      -0.002\n",
      "return_63d               -0.0018      0.000     -8.982      0.000      -0.002      -0.001\n",
      "return_1d_lag1            0.0010      0.000      7.430      0.000       0.001       0.001\n",
      "return_5d_lag1            0.0035      0.000     11.654      0.000       0.003       0.004\n",
      "return_10d_lag1           0.0008      0.000      1.714      0.087      -0.000       0.002\n",
      "return_21d_lag1           0.0020      0.000     11.721      0.000       0.002       0.002\n",
      "return_1d_lag2            0.0010      0.000      7.646      0.000       0.001       0.001\n",
      "return_5d_lag2        -5.578e-05      0.000     -0.167      0.867      -0.001       0.001\n",
      "return_10d_lag2          -0.0003      0.000     -0.847      0.397      -0.001       0.000\n",
      "return_21d_lag2           0.0006      0.000      4.492      0.000       0.000       0.001\n",
      "return_1d_lag3            0.0010      0.000      8.000      0.000       0.001       0.001\n",
      "return_5d_lag3         7.775e-05      0.000      0.232      0.816      -0.001       0.001\n",
      "return_10d_lag3           0.0005    6.4e-05      8.174      0.000       0.000       0.001\n",
      "return_21d_lag3          -0.0002   2.44e-05     -8.026      0.000      -0.000      -0.000\n",
      "return_1d_lag4            0.0011      0.000      8.047      0.000       0.001       0.001\n",
      "return_5d_lag4            0.0006      0.000      2.381      0.017       0.000       0.001\n",
      "return_10d_lag4        4.455e-05   4.51e-05      0.988      0.323   -4.38e-05       0.000\n",
      "return_21d_lag4        8.414e-05   2.41e-05      3.486      0.000    3.68e-05       0.000\n",
      "return_1d_lag5         1.121e-05   2.72e-05      0.413      0.680   -4.21e-05    6.45e-05\n",
      "return_5d_lag5            0.0005      0.000      1.779      0.075   -4.72e-05       0.001\n",
      "return_10d_lag5           0.0004   4.37e-05      8.424      0.000       0.000       0.000\n",
      "return_21d_lag5           0.0002   2.41e-05      7.773      0.000       0.000       0.000\n",
      "year_2014                -0.0004   3.68e-05     -9.674      0.000      -0.000      -0.000\n",
      "year_2015                -0.0006   4.02e-05    -13.802      0.000      -0.001      -0.000\n",
      "year_2016                -0.0003   3.93e-05     -8.246      0.000      -0.000      -0.000\n",
      "year_2017                -0.0002   3.86e-05     -5.234      0.000      -0.000      -0.000\n",
      "month_2                   0.0008   3.15e-05     26.798      0.000       0.001       0.001\n",
      "month_3                   0.0001   3.26e-05      3.391      0.001    4.67e-05       0.000\n",
      "month_4                   0.0003   3.23e-05      9.312      0.000       0.000       0.000\n",
      "month_5                   0.0005   3.18e-05     14.165      0.000       0.000       0.001\n",
      "month_6                   0.0002   3.22e-05      6.956      0.000       0.000       0.000\n",
      "month_7                   0.0004   3.35e-05     13.014      0.000       0.000       0.001\n",
      "month_8                 3.51e-05   3.38e-05      1.039      0.299   -3.11e-05       0.000\n",
      "month_9                   0.0003   3.33e-05      7.932      0.000       0.000       0.000\n",
      "month_10                  0.0004   3.42e-05     12.251      0.000       0.000       0.000\n",
      "month_11                  0.0004   3.33e-05     13.337      0.000       0.000       0.001\n",
      "month_12                  0.0001   3.25e-05      3.928      0.000     6.4e-05       0.000\n",
      "capital_goods             0.0011      0.000      6.697      0.000       0.001       0.001\n",
      "consumer_non-durables     0.0009      0.000      5.178      0.000       0.001       0.001\n",
      "consumer_services         0.0009      0.000      6.035      0.000       0.001       0.001\n",
      "energy                    0.0004      0.000      2.487      0.013     8.1e-05       0.001\n",
      "finance                   0.0010      0.000      6.508      0.000       0.001       0.001\n",
      "health_care               0.0008      0.000      5.238      0.000       0.000       0.001\n",
      "miscellaneous             0.0012      0.000      6.352      0.000       0.001       0.002\n",
      "public_utilities          0.0003      0.000      1.864      0.062   -1.79e-05       0.001\n",
      "technology                0.0011      0.000      7.757      0.000       0.001       0.001\n",
      "transportation            0.0010      0.000      5.918      0.000       0.001       0.001\n",
      "==============================================================================\n",
      "Omnibus:                    43620.237   Durbin-Watson:                   0.436\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          2371005.683\n",
      "Skew:                          -1.136   Prob(JB):                         0.00\n",
      "Kurtosis:                      25.665   Cond. No.                         80.6\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "target = 'target_5d'\n",
    "model = OLS(endog=y[target], exog=add_constant(X))\n",
    "trained_model = model.fit()\n",
    "print(trained_model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Obtain the residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:17:36.934452Z",
     "start_time": "2020-06-17T19:17:36.853213Z"
    }
   },
   "outputs": [],
   "source": [
    "preds = trained_model.predict(add_constant(X))\n",
    "residuals = y[target] - preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:17:38.443761Z",
     "start_time": "2020-06-17T19:17:37.230240Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(14,4))\n",
    "sns.distplot(residuals, fit=norm, ax=axes[0], axlabel='Residuals', label='Residuals')\n",
    "axes[0].set_title('Residual Distribution')\n",
    "axes[0].legend()\n",
    "plot_acf(residuals, lags=10, zero=False, ax=axes[1], title='Residual Autocorrelation')\n",
    "axes[1].set_xlabel('Lags')\n",
    "sns.despine()\n",
    "fig.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10-Day Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:16:47.657704Z",
     "start_time": "2020-06-17T19:16:47.418873Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             target_10d   R-squared:                       0.042\n",
      "Model:                            OLS   Adj. R-squared:                  0.042\n",
      "Method:                 Least Squares   F-statistic:                     86.05\n",
      "Date:                Wed, 17 Jun 2020   Prob (F-statistic):               0.00\n",
      "Time:                        15:16:47   Log-Likelihood:             4.1890e+05\n",
      "No. Observations:              109675   AIC:                        -8.377e+05\n",
      "Df Residuals:                  109618   BIC:                        -8.371e+05\n",
      "Df Model:                          56                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=========================================================================================\n",
      "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------\n",
      "const                    -0.0004    9.5e-05     -3.921      0.000      -0.001      -0.000\n",
      "rsi                       0.0002   5.85e-05      2.952      0.003     5.8e-05       0.000\n",
      "bb_high                   0.0006   6.85e-05      9.033      0.000       0.000       0.001\n",
      "bb_low                   -0.0002   6.55e-05     -3.360      0.001      -0.000   -9.17e-05\n",
      "atr                      -0.0002   2.01e-05    -11.787      0.000      -0.000      -0.000\n",
      "macd                     -0.0002   7.21e-05     -2.112      0.035      -0.000    -1.1e-05\n",
      "return_1d                 0.0006   9.01e-05      6.153      0.000       0.000       0.001\n",
      "return_5d              3.837e-05      0.000      0.136      0.892      -0.001       0.001\n",
      "return_10d               -0.0008      0.000     -2.731      0.006      -0.001      -0.000\n",
      "return_21d                0.0016      0.000     11.846      0.000       0.001       0.002\n",
      "return_42d               -0.0024      0.000    -13.261      0.000      -0.003      -0.002\n",
      "return_63d               -0.0014      0.000    -10.464      0.000      -0.002      -0.001\n",
      "return_1d_lag1            0.0005   8.98e-05      6.096      0.000       0.000       0.001\n",
      "return_5d_lag1            0.0013      0.000      6.109      0.000       0.001       0.002\n",
      "return_10d_lag1           0.0009      0.000      2.745      0.006       0.000       0.002\n",
      "return_21d_lag1           0.0017      0.000     14.828      0.000       0.002       0.002\n",
      "return_1d_lag2            0.0005   8.99e-05      6.075      0.000       0.000       0.001\n",
      "return_5d_lag2         4.183e-05      0.000      0.181      0.857      -0.000       0.000\n",
      "return_10d_lag2           0.0002      0.000      0.744      0.457      -0.000       0.001\n",
      "return_21d_lag2           0.0003   9.01e-05      2.909      0.004    8.55e-05       0.000\n",
      "return_1d_lag3            0.0006   9.03e-05      6.188      0.000       0.000       0.001\n",
      "return_5d_lag3        -2.034e-05      0.000     -0.088      0.930      -0.000       0.000\n",
      "return_10d_lag3           0.0005   4.44e-05     12.281      0.000       0.000       0.001\n",
      "return_21d_lag3          -0.0001   1.69e-05     -6.772      0.000      -0.000   -8.14e-05\n",
      "return_1d_lag4            0.0006   9.08e-05      6.402      0.000       0.000       0.001\n",
      "return_5d_lag4            0.0002      0.000      1.203      0.229      -0.000       0.001\n",
      "return_10d_lag4           0.0002   3.13e-05      7.453      0.000       0.000       0.000\n",
      "return_21d_lag4        9.158e-05   1.67e-05      5.469      0.000    5.88e-05       0.000\n",
      "return_1d_lag5        -1.801e-05   1.89e-05     -0.955      0.340    -5.5e-05     1.9e-05\n",
      "return_5d_lag5            0.0002      0.000      1.010      0.312      -0.000       0.001\n",
      "return_10d_lag5           0.0003   3.03e-05     10.676      0.000       0.000       0.000\n",
      "return_21d_lag5           0.0001   1.67e-05      8.425      0.000       0.000       0.000\n",
      "year_2014                -0.0004   2.55e-05    -14.026      0.000      -0.000      -0.000\n",
      "year_2015                -0.0006   2.79e-05    -20.574      0.000      -0.001      -0.001\n",
      "year_2016                -0.0002   2.73e-05     -9.091      0.000      -0.000      -0.000\n",
      "year_2017                -0.0002   2.68e-05     -7.951      0.000      -0.000      -0.000\n",
      "month_2                   0.0006   2.19e-05     27.855      0.000       0.001       0.001\n",
      "month_3                3.216e-05   2.26e-05      1.422      0.155   -1.22e-05    7.65e-05\n",
      "month_4                   0.0002   2.24e-05      7.710      0.000       0.000       0.000\n",
      "month_5                   0.0003   2.21e-05     14.059      0.000       0.000       0.000\n",
      "month_6                   0.0002   2.24e-05      7.706      0.000       0.000       0.000\n",
      "month_7                   0.0002   2.32e-05      7.871      0.000       0.000       0.000\n",
      "month_8               -6.384e-05   2.34e-05     -2.724      0.006      -0.000   -1.79e-05\n",
      "month_9                5.131e-05   2.31e-05      2.221      0.026    6.02e-06    9.66e-05\n",
      "month_10                  0.0003   2.37e-05     14.345      0.000       0.000       0.000\n",
      "month_11                  0.0002   2.31e-05      9.916      0.000       0.000       0.000\n",
      "month_12              -4.164e-05   2.26e-05     -1.846      0.065   -8.59e-05    2.58e-06\n",
      "capital_goods             0.0010      0.000      8.684      0.000       0.001       0.001\n",
      "consumer_non-durables     0.0007      0.000      6.398      0.000       0.001       0.001\n",
      "consumer_services         0.0007      0.000      7.208      0.000       0.001       0.001\n",
      "energy                    0.0003      0.000      2.735      0.006    8.26e-05       0.001\n",
      "finance                   0.0009      0.000      8.316      0.000       0.001       0.001\n",
      "health_care               0.0006      0.000      6.167      0.000       0.000       0.001\n",
      "miscellaneous             0.0012      0.000      8.852      0.000       0.001       0.001\n",
      "public_utilities          0.0002      0.000      1.617      0.106    -4.4e-05       0.000\n",
      "technology                0.0010      0.000      9.982      0.000       0.001       0.001\n",
      "transportation            0.0010      0.000      7.926      0.000       0.001       0.001\n",
      "==============================================================================\n",
      "Omnibus:                    38434.145   Durbin-Watson:                   0.233\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          1114460.653\n",
      "Skew:                          -1.085   Prob(JB):                         0.00\n",
      "Kurtosis:                      18.465   Cond. No.                         80.6\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "target = 'target_10d'\n",
    "model = OLS(endog=y[target], exog=add_constant(X))\n",
    "trained_model = model.fit()\n",
    "print(trained_model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Monthly Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:16:48.672073Z",
     "start_time": "2020-06-17T19:16:48.424866Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             target_21d   R-squared:                       0.059\n",
      "Model:                            OLS   Adj. R-squared:                  0.058\n",
      "Method:                 Least Squares   F-statistic:                     121.7\n",
      "Date:                Wed, 17 Jun 2020   Prob (F-statistic):               0.00\n",
      "Time:                        15:16:48   Log-Likelihood:             4.6155e+05\n",
      "No. Observations:              109675   AIC:                        -9.230e+05\n",
      "Df Residuals:                  109618   BIC:                        -9.224e+05\n",
      "Df Model:                          56                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=========================================================================================\n",
      "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------\n",
      "const                    -0.0003   6.44e-05     -4.358      0.000      -0.000      -0.000\n",
      "rsi                    2.341e-05   3.96e-05      0.591      0.555   -5.43e-05       0.000\n",
      "bb_high                   0.0002   4.64e-05      3.585      0.000    7.55e-05       0.000\n",
      "bb_low                    0.0002   4.44e-05      4.169      0.000    9.81e-05       0.000\n",
      "atr                      -0.0002   1.36e-05    -15.358      0.000      -0.000      -0.000\n",
      "macd                     -0.0003   4.89e-05     -5.224      0.000      -0.000      -0.000\n",
      "return_1d                 0.0003    6.1e-05      4.898      0.000       0.000       0.000\n",
      "return_5d                -0.0005      0.000     -2.607      0.009      -0.001      -0.000\n",
      "return_10d               -0.0003      0.000     -1.328      0.184      -0.001       0.000\n",
      "return_21d                0.0018   9.41e-05     18.679      0.000       0.002       0.002\n",
      "return_42d               -0.0018      0.000    -14.230      0.000      -0.002      -0.002\n",
      "return_63d               -0.0012   9.31e-05    -12.738      0.000      -0.001      -0.001\n",
      "return_1d_lag1            0.0003   6.09e-05      5.063      0.000       0.000       0.000\n",
      "return_5d_lag1            0.0003      0.000      2.395      0.017    6.12e-05       0.001\n",
      "return_10d_lag1           0.0001      0.000      0.500      0.617      -0.000       0.001\n",
      "return_21d_lag1           0.0017   7.93e-05     21.293      0.000       0.002       0.002\n",
      "return_1d_lag2            0.0003   6.09e-05      5.544      0.000       0.000       0.000\n",
      "return_5d_lag2         6.265e-05      0.000      0.399      0.690      -0.000       0.000\n",
      "return_10d_lag2           0.0001      0.000      0.739      0.460      -0.000       0.000\n",
      "return_21d_lag2           0.0005   6.11e-05      7.955      0.000       0.000       0.001\n",
      "return_1d_lag3            0.0004   6.12e-05      6.007      0.000       0.000       0.000\n",
      "return_5d_lag3            0.0001      0.000      0.643      0.520      -0.000       0.000\n",
      "return_10d_lag3           0.0001   3.01e-05      4.172      0.000    6.66e-05       0.000\n",
      "return_21d_lag3       -8.538e-05   1.15e-05     -7.446      0.000      -0.000   -6.29e-05\n",
      "return_1d_lag4            0.0004   6.16e-05      6.239      0.000       0.000       0.001\n",
      "return_5d_lag4         4.801e-05      0.000      0.382      0.703      -0.000       0.000\n",
      "return_10d_lag4        3.879e-05   2.12e-05      1.830      0.067   -2.75e-06    8.03e-05\n",
      "return_21d_lag4           0.0001   1.13e-05     13.028      0.000       0.000       0.000\n",
      "return_1d_lag5         3.325e-06   1.28e-05      0.260      0.795   -2.17e-05    2.84e-05\n",
      "return_5d_lag5        -1.251e-05      0.000     -0.102      0.919      -0.000       0.000\n",
      "return_10d_lag5        6.135e-06   2.05e-05      0.299      0.765   -3.41e-05    4.64e-05\n",
      "return_21d_lag5       -3.558e-05   1.13e-05     -3.145      0.002   -5.78e-05   -1.34e-05\n",
      "year_2014                -0.0003   1.73e-05    -17.181      0.000      -0.000      -0.000\n",
      "year_2015                -0.0006   1.89e-05    -29.887      0.000      -0.001      -0.001\n",
      "year_2016                -0.0002   1.85e-05     -9.737      0.000      -0.000      -0.000\n",
      "year_2017                -0.0002   1.82e-05    -10.311      0.000      -0.000      -0.000\n",
      "month_2                8.443e-05   1.48e-05      5.700      0.000    5.54e-05       0.000\n",
      "month_3                  -0.0002   1.53e-05     -9.795      0.000      -0.000      -0.000\n",
      "month_4                  -0.0001   1.52e-05     -8.102      0.000      -0.000   -9.32e-05\n",
      "month_5                2.202e-05    1.5e-05      1.472      0.141    -7.3e-06    5.14e-05\n",
      "month_6               -2.039e-05   1.52e-05     -1.345      0.179   -5.01e-05    9.32e-06\n",
      "month_7                  -0.0003   1.57e-05    -18.480      0.000      -0.000      -0.000\n",
      "month_8                  -0.0002   1.59e-05    -14.883      0.000      -0.000      -0.000\n",
      "month_9                  -0.0002   1.57e-05    -11.602      0.000      -0.000      -0.000\n",
      "month_10               6.587e-05   1.61e-05      4.092      0.000    3.43e-05    9.74e-05\n",
      "month_11                 -0.0001   1.57e-05     -7.552      0.000      -0.000   -8.76e-05\n",
      "month_12                 -0.0004   1.53e-05    -26.081      0.000      -0.000      -0.000\n",
      "capital_goods             0.0008   7.57e-05     11.197      0.000       0.001       0.001\n",
      "consumer_non-durables     0.0007   7.84e-05      8.618      0.000       0.001       0.001\n",
      "consumer_services         0.0006   7.01e-05      8.932      0.000       0.000       0.001\n",
      "energy                    0.0002   7.22e-05      2.652      0.008    4.99e-05       0.000\n",
      "finance                   0.0008   7.37e-05     10.793      0.000       0.001       0.001\n",
      "health_care               0.0005   6.89e-05      7.493      0.000       0.000       0.001\n",
      "miscellaneous             0.0012   9.02e-05     12.968      0.000       0.001       0.001\n",
      "public_utilities          0.0001   8.71e-05      1.241      0.215   -6.26e-05       0.000\n",
      "technology                0.0009   6.95e-05     13.417      0.000       0.001       0.001\n",
      "transportation            0.0009   8.24e-05     11.164      0.000       0.001       0.001\n",
      "==============================================================================\n",
      "Omnibus:                    40145.029   Durbin-Watson:                   0.129\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           847211.689\n",
      "Skew:                          -1.252   Prob(JB):                         0.00\n",
      "Kurtosis:                      16.384   Cond. No.                         80.6\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "target = 'target_21d'\n",
    "model = OLS(endog=y[target], exog=add_constant(X))\n",
    "trained_model = model.fit()\n",
    "print(trained_model.summary())"
   ]
  }
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